Lef1 ablation alleviates cartilage mineralization following posttraumatic osteoarthritis induction

Significance Cartilage mineralization is imperative in various processes such as skeletal growth and fracture repair. However, this process may also be pathological, as in the case of the degenerative joint disease, osteoarthritis (OA). Using a posttraumatic OA model (PTOA), we find that cartilage-specific Sirt1 genetic nulls caused severe synovitis and mineralization of the lateral joint compartment, due to augmented Lef1 gene expression. Conversely, cartilage-specific Lef1 nulls exhibited impaired synovitis and mineralization of the lateral joint, accompanied by a reduction of local pain. Consistently, transcriptomic profiles of Lef1-ablated chondrocytes exhibited enhanced anabolism, yet impaired pathways related to calcification and inflammation. Accordingly, cartilage mineralization of the lateral joint compartment relies on amplified inflammatory pathways, contributing to articular damage following PTOA.


Immunohistochemistry, immunofluorescence and histological procedures
Immunohistochemistry (IHC) for harvested murine joints was carried out following 2 days of tissue fixation in 4% paraformaldehyde in PBS and 21 days of decalcification with 10% EDTA (Sigma-Aldrich) in double distilled water (DDW) at (pH=7.4). Samples were then dehydrated using a graded series of ethanol washes, embedded in paraffin, and sectioned to 7-μm slices.
Sequential coronal sections were obtained until reaching the medial to lateral plane (approx. 1500M in depth, based on the embedded sample orientation). Then 20 slides (2 sections per slide) at 7m thickness each, were obtained for staining procedures. Sections were digested with 1 mg/mL hyaluronidase (Sigma-Aldrich, cat#H3506) in PBS at pH=6 for 1 h at 37 o C and incubated overnight with primary antibodies; MMP13 (cat# ab39012, Abcam, UK), F4/80 CI:A3-1 (cat# MCA497GA, Biorad laboratories, CA); COL1A1 (cat# ab21286, Abcam). ZytoChem Plus HRP polymer conjugated anti-rabbit (cat#ZUC032, Zytomed Systems, Germany) was used as a secondary antibody followed by DAB substrate kit (cat#550880, BD Pharmingen, CA) for color development. Negative controls were incubated with secondary antibody alone and counterstained with hematoxylin. IHC sections stained with MMP13, F4/80 CI:A3-1 and COL1A1 were quantified by ImageJ IHC_Toolbox.jar plugin per standard analyzed area to detect staining intensity. COL1A1 staining was assessed via two standard boxes created in SI_1, Elayyan et al., 2022 6 ImageJ software for external and internal meniscal sites of the lateral compartment (see Fig.   5).
The following day, sections were incubated with Alexa flour 568 anti-rabbit (1:1000 in blocking solution, #cat A-11011, UK, Abcam), for 2 h at RT. Finally, sections were incubated with DAPI (final 5ng/L in DDW) for 10min at RT, washed four times with PBS and mounted (IMMU-MOUNT, MA, Thermo Fisher). Sections were visualized under a Ti-Eclipse Nikon system with an Andor Zyla nsc05537 camera (Japan, Nikon). Captured depictions were analyzed for coappearance of DAPI/LEF1 intensity and % positive nuclei, using NIS-elements BR software (Japan, Nikon).
To determine OA histopathology in murine joints, sections were stained with 0.5% Safranin O (cat# 1.15948, Millipore) and 0.1% Fast green (cat# 1.04022, Millipore) after using Wiegert's Iron Hematoxylin (cat# 1.15973, Merck) in DDW. OA histopathology scoring was carried out based on the 0-6 scale for OA severity (6 denoting the most severe case), according to a Glasson et al. 2010 method [9]. For OARSI severity scoring we obtained 1-3 sequential (80m-spaced) coronal sections, which were stained and graded by three blinded graders, according to the scoring criteria of Glasson et al. 2010. Average scores of graders are presented in graphs. Notably, OA severity between females vs. males for all genotypes (i.e., ATC Sirt1 fl/fl , Sirt1 fl/fl , ATC Lef1 fl/fl and Lef1 fl/fl ; sham or PTOA) were insignificant (SI_2 : table b; table c, respectively).
Osteophyte formation was monitored on a 0-3 scale (3 denoting the most severe case), based on the method by Kamekura S.,et al.[10]. Additionally, the degree of synovitis was determined based on synovial thickness and appearance of F4/80 positive cells within the synovial membrane area. Briefly, synovial lining was stained by H&E staining (Mayer's Hematoxylin, cat# 1100, Kaltek; Eosin Y, cat# 3801601E, Leica Biosystems) and the membrane region was extracted manually from each joint image, to be subsequently uploaded to ImageJ software. Using ImageJ processing tools, the number of colored pixels were converted to cm 2 , to indicate the thickness of the selected synovial membrane area. Secondly, IHC of F4/80+ (brown) macrophages was carried out and quantified by ImageJ:IHC_Toolbox.jar plugin, per analyzed area for F4/80 positive cells.

Micro computed tomography (CT) Analysis
Harvested joints were fixed in 4% paraformaldehyde solution for 48h and subsequently stored in 70% EtOH, for μCT assessments of calcified tissue using Skyscan 1174 (Bruker, Belgium; 50 kV,0.25 aluminum filter, 4000 ms exposure time, 6.4 μm ,0.4° rotation angle, 7.95 μm pixel size). Scans were reconstructed in NRecon software (Bruker, Belgium). The scans were converted to longitudinal sections and the area of interest was selected as the tibial subchondral bone plate and mineralized meniscus using CTan software (Bruker, Belgium). Compartments were manually segmented into medial and lateral regions of interest (ROIs). Three-dimensional (3D) analysis was assessed to quantify bone volume of mineralized meniscal tissue and percent of bone volume/tissue volume (%BV/TV) and cortical thickness of tibial subchondral cortical bone plate using CTan software. The data obtained was analyzed and compared between PTOA ATC Sirt1 fl/fl and Sirt1 fl/fl ; or PTOA ATC Lef1 fl/fl and Lef1 fl/fl mice, as well as respective sham controls. 3D images were produced with Dragonfly software (ORS, Montreal, Canada). SI_1, Elayyan et al., 2022 8

RNA isolation and qPCR analysis
Total RNA was extracted from cells using the RNeasy kit (Qiagen, Valencia, CA, USA). Oligo dT was used as the primer in the reverse-transcription reaction. Quantitative real-time PCR (qPCR) reactions were performed with 10 ng of cDNA and Syber Green mix (BioRad Laboratories, Hercules, CA, USA). Quantitative analyses were performed with iCycler software (Bio-Rad, Hercules, CA, USA). All RNA samples were treated with DNase I before the PCR reactions. Human and mouse primers used for gene expression and genotyping are in SI_2 (table d). Values were normalized to GAPDH, which remained unaffected by the experimental treatments.

RNAseq Data Acquisition
Mice costal chondrocytes derived from Lef1 fl/fl (n=3), ATC Lef1 fl/fl (n=3), and ATC Sirt1 fl/fl (n=2) were extracted, cultured and plated to reach passage 2. Following 24h doxycycline treatment, total RNA was isolated and assessed for quality according to EMBL guidelines using a TapeStation Analysis Software A.02.02 (SR1). RNA integrity numbers (RIN) for all samples were > 9.3. Plate-based RNA-Seq with poly(A) selection sample prep was performed using the KAPA Stranded RNASeq Library Preparation Kit (Illumina® platforms), product codes KK8400 and KK8401. For quality control of RNA yield and library synthesis products, the RNA ScreenTape and D1000 ScreenTape kits (both from Agilent Technologies), Qubit® RNA HS Assay kit, and Qubit® DNA HS Assay kit (both from Invitrogen) were used for each specific step. mRNA libraries were prepared from 1 µg RNA using the KAPA Stranded mRNA-Seq Kit, with mRNA Capture Beads (kapabiosystems, KK8421, https://www.kapabiosystems.com/).
Each library was eluted in 20 µl elution buffer, pooled libraries was adjusted to 10 mM. The multiplexed sample pool (1.6 pM including PhiX 1%) was loaded on NextSeq 500/550 High Output v2 kit (75 cycles) cartridge, and loaded onto the NextSeq 500 System (Illumina, San SI_1, Elayyan et al., 2022 9 Diego, CA, USA), with 75 cycles and single-read sequencing conditions. Sequencing data sets have been deposited in the GEO database-accession number (GSE200522). Prior to sequence analysis, quality control of RNA-Seq samples was performed on the raw single-end reads using Fastqx to remove low-quality reads (average score less than 30). Reads that were less than 20 base pairs were discarded. In addition, all reads were cut at the ends, leaving the sequence between 14-54 base pair, due to low-quality base readings. Reads were then aligned to the Mus Musculus genome GRCm38 version with STAR [11], using standard presets except for intron size, which was set between 30 and 30000 bp (-alignIntronMin 30 and -alignIntronMax 30000). Greater than 10 million reads were mapped for each sample with uniquely mapping reads accounting for ∼90% of total mapped reads in each sample. Uniquely mapping reads were count using HT-Seq [12]. Prior to analysis, PCA plot was validated to determine that datasets are sufficiently variable. Next, differential expression analysis was and articular chondrocytes established a significant variation on the PC1 axis, which is the reason for our assessment of expression and staining profiles amongst both cell types, as indicated within the results section ( Fig.1; Fig. 3; SI_3; SI_12). Differentially expressed genes were calculated by 1-fold expression difference or greater (Log2 fold expression) compared to the Lef1 fl/fl control or ATC Sirt1 fl/fl compared to ATC Lef1 fl/fl values, with an adjusted p-value lower than 0.1, which were presented in tables and heatmaps according to classification categories of interest.
Functional annotations analysis was preformed to assign biological relevance of the differentially expressed gene sets, derived from DESeq2 analysis and subjected to DAVID Bioinformatic resources 6.8 [16]. Employing DAVID tool enabled the analysis of gene clusters contributing to particular biological processes, which display differential amongst the Lef1 fl/fl , and ATC Lef1 fl/fl . Next, DAVID datasets were filtered for GO enrichment pathways, which were plotted in a Venn diagram for upregulated and downregulated GO enrichment gene sets (SI_4), wherein 5 categories were common and omitted from the next steps of analysis. As such, enrichment categories were plotted in GO classification (i.e. Molecular function, Biological process, Cellular component), with a total of 25 downregulated sets and, 20 upregulated sets (SI_4). Of those unique pathways, we identified common functional processes which were upregulated (i.e. WNT signaling, DNA binding and signaling, carbohydrate processing) or down regulated (i.e. Inflammation), separately. To identify key interactions and regulatory molecules from those processes, we employed STRING network analysis (SI_5; version 11.5 https://string-db.org/ [17]) with a common threshold of 0.4. Central nodes (Hub nodes) were identified as such by using cytoHubba (version 0.1, [18]) plugin in the Cytoscape software (version 3.8.2 [19]) based on the Degree topological analysis method. Relevant tables are presented in SI_6-9. For ATC Sirt1 fl/fl vs ATC Lef1 fl/fl comparisons (SI_3E, SI_3F), we employed DAVID datasets, which were filtered for GO enrichment cluster related to "Extracellular Region" and plotted based on the 10 most differentially expressed genes (Fig. 4G, SI_10).

Statistical analysis
Each experiment was repeated at least three times (n ≥ 3) and the average and standard deviation was calculated per group, after removing statistically significant outliers, as determined by GraphPad prism software. All the data were analysed for non-parametric Kruskal-Wallis and post-hoc Dunn's test to assess statistical significance within a group of treatments, assuming a p<0.05. After confirming significance of Kruskal-Wallis test, we employed Mann-Whitney test to assess statistical significance between treatments. Statistical significance according to Mann-Whitney test is denoted with an asterisk (*) for p<0.05 above the relevant bar, or as indicated in figure legends. Pearson correlation was assessed for mice joint parameters, assuming a confidence level greater than 95% (p<0.05), to be significant.
Notably, Pearson's correlation (r) that is closer to 1 indicates a good fit to linear regression, while values closer to 0 indicate weak fit to linear regression. Regression (r 2 ) indicates the variation around the linear regression line. The cumulative scores of OA severity per compartment (0-12) were plotted for the medial vs lateral correlations. Lateral Osseous Remodeling (LOR) was calculated by adding the cumulative score of osteophyte grading (0-6 for both tibia and femoral compartments) to the mineralized meniscal value (in 10nm 3 , as determined by microCT) only for the lateral compartment (0-4), on a total range of 0-10 for xaxis. Next, we comparted the LOR measure, with Pain thresholds (range 0-400gf) or medial or lateral OA severity (0-12), as indicated above. Graphs and statistical analysis were carried out in GraphPad Prism 9.0 software, while illustrations carried out via BioRender software.           Chondrogenic genes and Cartilage (DAVID);  Fig.3G in a z-score heatmap.

SI_6: Table a-d:
Differentially altered genes by genes clusters for ATC Lef1 fl/fl vs Lef1 fl/fl . Asterisks (*) denotes genes appearing under both "Chondrogenic" classification and "Cartilage" gene clusters based on DAVID annotation

SI 10: Extracellular Region GO Enrichment cluster for ATC Sirt11 fl/fl vs ATC Lef1 fl/fl :
Costal chondrocytes from ATC Sirt11 fl/fl (n=2) vs ATC Lef1 fl/fl (n=3) transgenes were isolated and processed for RNAseq and subjected to bioinformatic as indicated in the materials and methods section. GO enrichment gene cluster was obtained for the Extracellular Region classification, to determine differentially expressed genes that may be secreted from cartilage. Table a. Upregulated genes with the 10 most differentially increased LFC in dark green highlight, Table b. Downregulated genes with the 10 most differentially decreased LFC in dark orange highlight (Fig. 4G graphical depiction).      classification, to determined differentially expressed genes that may be secreted from cartilage. Table a. Upregulated genes with the 10 most differentially increased LFC in dark green highlight, Table b. Downregulated genes with the 10 most differentially decreased LFC in dark orange highlight (Fig. 4G graphical depiction).

SI_11: Combined activation of Sirt1 and blockage of cathepsin B prevented PTOA:
Here we employed an experimental model protrayed in Fig. 1F. Breifly, PTOA mice (Female, three months old, n≥5-7 per group) were injected twice a week with vehicle (20 %v/v DMSO in PBS), SRT1720 and\or CA074me, from week 4-8 post surgical procedure, as indicated in intensity within the DAPI positive nuclei (right graph) of PTOA mice treated with vehicle, SRT1720, CA074me, and SRT1720 + CA074me. Representative immunofluorescent images are presented to the right of the graphs (x40 magnification). (F) Collgen type 1 staining for the lateral menisci of PTOA mice treated with vehicle, SRT1720, CA074Me, and SRT1720 + CA074me. Left graph is of the internal region and right graph is of the external region (illustration on the right of the representative immune-stained images). Statistical significance determined by Kruskal Wallis test followed by Mann and Whitney for paired treatments, assuming confidence for p<0.05 (*) and p<0.001 (**).